Universal knowledge engines for lexical semantic creativity in humans and machines

本研究旨在研究世界语言词汇中共享的知识结构，从而开发通用知识引擎，以增强机器对人类语言的解释。区分人类语言使用者与人工自然语言系统的一个关键方面是我们解释和创造新单词用法的能力，这种创造性特征反映在世界语言的词汇语义结构中。这种现象被称为词义扩展，它对机器理解自然语言最基本的单位——词——提出了重大挑战。这个合作研究项目将通过两个主要步骤，为弥合人类和机器在词汇语义创造力方面的差距做出新的努力。1)我们将通过儿童语言习得，从包括跨语言数据库、心理实验和深度学习模型在内的各种公共场所绘制数据和资源，在世界语言中共享的词义扩展中的大规模知识表征。2)我们将开发新的方法，利用和整合不同的知识模式来构建通用知识引擎，以支持跨语言的灵活和自适应的新词义解释和推理。

This proposed research investigates knowledge structures that are shared in the lexicons across the world's languages and thereby aims to develop universal knowledge engines that will enhance machine interpretation of human languages.A key aspect that distinguishes human language users from artificial natural language systems is our ability to interpret and create novel word usages, and this creative character is reflected in the lexical semantic structures of the world's languages. This phenomenon, known as word meaning extension, poses significant challenges for machine understanding of the most fundamental units of natural languages: words. This collaborative research program will launch a new effort in bridging the gap between humans and machines in lexical semantic creativity, by taking two main steps. 1) We will characterize at a large scale knowledge representations in word meaning extension that are shared across the world's languages and through child language acquisition, drawing data and resources from a variety of publicly accessible venues including cross-linguistic databases, psychological experiments, and deep learning models. 2) We will develop new methodologies that harness and integrate different knowledge modalities toward constructing universal knowledge engines to support the flexible and adaptive interpretation and inference of novel word meanings across languages.